Watching the Memory Tightrope: A Real-Time Stress Test of the cuzk Budget Manager

In the high-stakes world of GPU-accelerated proof generation for the Filecoin network, memory is the most precious and unforgiving resource. A single miscalculation can crash a proving daemon that has been running for hours, wasting GPU work and delaying critical proof submissions. Message [msg 2398] captures a pivotal moment in the testing of a newly designed unified memory manager for the cuzk proving engine — a moment where months of architectural design, debugging, and configuration tuning converge into a single, tense observation window. The assistant watches RSS climb toward the danger zone, analyzes why the numbers don't match the budget, and makes the critical decision to hold steady rather than intervene.

The Context: A Budget System Under Fire

To understand the significance of this message, one must appreciate what came before it. The assistant had spent several sessions (segments 14 through 18 of the conversation) designing and implementing a comprehensive memory management architecture for cuzk. The old system used a static partition_workers semaphore — a fixed concurrency limit that bore no relation to actual memory consumption. This was fragile: it either underutilized memory (leaving throughput on the table) or overcommitted and caused OOM kills. The new system replaced this with a byte-level budget: a total_budget parameter (set in configuration) that the engine would track against, using an LRU evictor for SRS and PCE caches and a two-phase working memory release protocol.

The remote test machine had 755 GiB of RAM, but roughly 226 GiB was consumed by a co-resident Curio process. That left approximately 529 GiB for cuzk. After an earlier OOM failure with an auto budget (which defaulted to 750 GiB, causing cuzk to compete fatally with Curio), the assistant reconfigured to an explicit 400 GiB budget — a deliberately conservative figure that should allow roughly 24 concurrent partitions while leaving 129 GiB of headroom for the OS and allocator overhead.

The assistant had just committed a critical try_lock() fix for the evictor callback (see [msg 2378]), which prevented a panic when the evictor ran from an async context and attempted to call blocking_lock() on a tokio Mutex. With that fix in place and the 400 GiB budget configured, the daemon was started, and a bench test with 3 proofs (30 partitions total, using --concurrency 3) was launched.

The Message: Reading the Vital Signs

Message [msg 2398] is the assistant's first status check after the bench began running. It is structured as a real-time diagnostic report, combining log data, RSS traces, and the assistant's own analytical commentary. The assistant opens with a cautious but optimistic declaration — "It's working!" — before immediately tempering it with a detailed list of observations that reveal a more complex picture.

The RSS trace tells a dramatic story. In the span of about 10 minutes, the cuzk daemon's memory consumption rocketed from a baseline of 12,516 kB (essentially idle) to 487,925,596 kB — roughly 487 GiB. The trace shows a nearly linear climb: 31 GiB at 14:43:46, 67 GiB ten seconds later, 113 GiB, 158 GiB, and so on, peaking at 487 GiB at 14:44:46. After the peak, the RSS begins a slow decline, settling into a range between 397 GiB and 425 GiB.

The daemon log confirms what is driving this memory usage: multiple partitions are synthesizing concurrently (jobs with IDs like 9f2e06d4, 058cd703, 01a1e4fe), the first GPU proof has completed (partition 0 in 5021ms), and two PCE extractions have finished, each consuming approximately 59 seconds and 25.7 GiB. The SRS parameters have been loaded implicitly — the GPU work could not proceed without them.

The Analytical Framework: Why RSS Exceeds Budget

The most intellectually interesting part of the message is the assistant's analysis of the discrepancy between the 400 GiB budget and the 487 GiB RSS. This is not a simple bug — it is a fundamental tension between the budget model and real-world memory behavior. The assistant identifies three contributing factors:

First, CUDA pinned memory. The SRS (Structured Reference String) parameters, which are large elliptic curve data structures used for proof generation, are loaded into CUDA pinned (page-locked) memory. This memory is allocated by the CUDA driver and may not be fully visible to the operating system's RSS tracking in the same way that regular heap allocations are. More importantly, the budget manager tracks allocations at the application level — it knows how much it thinks it has allocated for SRS, but CUDA's internal memory management can introduce overhead that the budget never sees.

Second, allocator fragmentation and overhead. The Rust memory allocator (jemalloc by default) maintains its own internal structures: arenas, chunks, runs, and metadata. When the application allocates and frees hundreds of gigabytes of proof data across 30 partitions, the allocator may hold onto memory that has been returned to it but not yet released to the OS. This is especially pronounced in workloads with large, short-lived allocations — exactly the pattern of partition synthesis, where each partition allocates ~13.6 GiB for its working set and then frees it upon completion.

Third, the SRS double-acquisition race. Earlier in the session (segment 17), the assistant had diagnosed a concurrency issue where two partitions could race to acquire the SRS simultaneously, temporarily consuming extra budget. While the try_lock() fix addressed the evictor panic, the underlying race condition for SRS acquisition could still cause momentary budget overcommitment.

The Decision: To Wait or To Act

The assistant's decision at the end of the message is subtle but significant. Rather than panicking at the 487 GiB RSS reading — which is only 42 GiB below the 529 GiB ceiling — the assistant chooses to continue monitoring. The reasoning is implicit but clear: the RSS has already begun to decline from its peak (487 GiB → 475 GiB → 444 GiB in subsequent readings), suggesting that the spike was a transient phenomenon caused by overlapping allocations during the initial burst of partition starts. The system appears to be self-correcting as partitions complete and release their working memory.

This decision reflects a deep understanding of the system's dynamics. The assistant knows that the two-phase memory release protocol (designed in segment 14 and implemented in segment 16) ensures that GPU memory is freed before CPU-side synthesis memory, and that the LRU evictor will reclaim SRS and PCE cache entries when the budget is tight. The RSS overshoot is concerning but not catastrophic — it is the difference between the budget's accounted memory and the allocator's actual memory, and the gap should close as the allocator consolidates freed regions.

There is also a pragmatic calculation at play: killing the process and restarting would lose all the progress made — the SRS loaded, the PCE extracted, the first GPU proof completed. The marginal risk of an OOM (which would kill the process anyway) is worth taking if there is a reasonable chance the system stabilizes. And the declining RSS trend supports that gamble.

Assumptions and Their Risks

The assistant makes several assumptions in this message, most of which are reasonable but worth examining.

The first assumption is that the RSS peak of 487 GiB represents the worst case. The assistant notes "now at ~478 GiB" and the trace shows a downward trajectory. But the trace also shows a second peak at 14:45:46 (425 GiB, up from 413 GiB five seconds earlier), suggesting that memory pressure is not monotonic — new partitions starting can push RSS back up. If the bench test has 30 partitions and only 3 are running concurrently, the system could see multiple waves of allocation as each batch of 3 progresses through synthesis and GPU proving.

The second assumption is that the budget accounting discrepancy is bounded. The assistant lists three causes for the gap but does not quantify their maximum impact. CUDA pinned memory for SRS is estimated at ~44 GiB, allocator overhead might add another 10-20%, and the double-acquisition race is transient. But these are estimates, not measurements. If the gap grows with the number of concurrent partitions, a test with higher concurrency could push RSS past the 529 GiB ceiling.

The third assumption is that the co-resident Curio process will remain stable at 226 GiB. The assistant's free -h output in a subsequent check would reveal whether Curio's memory usage fluctuates. If Curio allocates additional memory during the test (e.g., for garbage collection or sector processing), the available headroom for cuzk shrinks, potentially triggering an OOM even if cuzk's RSS stays flat.

Input Knowledge Required

To fully understand this message, a reader needs knowledge spanning several domains. First, the architecture of the cuzk proving engine: the distinction between SRS (global parameters loaded once and shared), PCE (pre-compiled constraint evaluators extracted per-proof-type), synthesis (CPU-side constraint generation), and GPU proving (the actual Groth16 proof computation). Second, the memory manager design: the total_budget parameter, the LRU evictor, the two-phase release protocol, and the distinction between accounted budget and actual RSS. Third, the operational context: the remote machine's 755 GiB RAM, the 226 GiB consumed by Curio, the 529 GiB available ceiling, and the 400 GiB configured budget. Fourth, the recent history: the try_lock() evictor fix, the earlier OOM with auto budget, and the SRS double-acquisition race.

Output Knowledge Created

This message generates several important pieces of knowledge. First, it provides real-world validation that the budget manager can keep the system alive under load — the daemon did not crash, the GPU proof completed, and the RSS began to decline. Second, it quantifies the gap between budget accounting and actual RSS for this workload: roughly 87 GiB (487 GiB RSS vs. 400 GiB budget) at the peak, narrowing to about 25 GiB (425 GiB vs. 400 GiB) during steady state. Third, it establishes that the RSS trajectory under the budget manager is non-monotonic and wave-like, reflecting the batch concurrency pattern of the bench test. Fourth, it identifies CUDA pinned memory as a significant source of untracked RSS — a finding that could inform future improvements to the budget model, such as adding a CUDA-aware memory estimation or a configurable "pinned memory overhead" parameter.

The Thinking Process

The assistant's reasoning in this message follows a clear diagnostic pattern: observe, compare, hypothesize, decide. The observation is the RSS trace showing a dramatic climb to 487 GiB. The comparison is against the known constraints: the 400 GiB budget and the 529 GiB ceiling. The hypothesis is the three-factor explanation for the discrepancy. The decision is to continue monitoring rather than intervene.

What is notable is what the assistant does not do. It does not immediately kill the process. It does not adjust the budget downward. It does not reduce the concurrency. It does not query the daemon's internal budget state to see exactly how much is accounted. These omissions are not mistakes — they reflect a deliberate strategy of minimal intervention. The system was designed to self-regulate through the evictor and the two-phase release protocol. Intervening prematurely would undermine the very test the assistant is running: a validation that the budget manager can handle real-world load without human hand-holding.

The assistant also demonstrates a sophisticated understanding of which metrics matter. It focuses on RSS (from /proc/PID/status) rather than the daemon's own memory accounting, because RSS is what the OOM killer sees. The budget is a control signal; RSS is the ground truth. The gap between them is not a bug but a feature of the abstraction — and the assistant's willingness to tolerate that gap, within bounds, is a mark of engineering maturity.

Conclusion

Message [msg 2398] is a snapshot of a system under stress and an engineer reading its vital signs with a steady hand. It captures the moment when theory meets reality: the elegant budget manager, designed in abstract, collides with the messy physics of CUDA pinned memory, allocator fragmentation, and concurrent partition lifecycles. The assistant's analysis is precise, its assumptions are reasoned, and its decision to wait rather than act reflects a deep trust in the system's design. The message is not just a status update — it is a case study in how to monitor, interpret, and respond to complex system behavior under real-world load.